Take Home Exercise 5

Let’s see what the maps have to say

Objective

The objective of this take home exercise is to understand the GeoVisualization using tmaps and answer some of the questions presented in the VAST Challenge 2022

• Social Areas
• Traffic areas

Setting up the scene

packages = c('rmarkdown','sf','tmap','tidyverse','clock','lubridate','sftime','lwgeom')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

Here we are reading the files in two formats- sf and csv. This is done so that functions such as inner join can be performed and also the data can be used to create tmaps.

schoolscsv <- read_csv("data/Schools.csv")

pubcsv<-read_csv("data/Pubs.csv")

apartmentscsv<-read_csv("data/Apartments.csv")
buildingscsv<-read_csv("data/buildings.csv")
employerscsv<-read_csv("data/Employers.csv")
restaurantcsv<-read_csv("data/Restaurants.csv")
schools <- read_sf("data/Schools.csv", 
                   options = "GEOM_POSSIBLE_NAMES=location")

pubs<-read_sf("data/Pubs.csv", 
                   options = "GEOM_POSSIBLE_NAMES=location")

apartments<-read_sf("data/Apartments.csv", 
                   options = "GEOM_POSSIBLE_NAMES=location")
buildings<-read_sf("data/buildings.csv", 
                   options = "GEOM_POSSIBLE_NAMES=location")
employers<-read_sf("data/Employers.csv", 
                   options = "GEOM_POSSIBLE_NAMES=location")
restaurant<-read_sf("data/Restaurants.csv", 
                   options = "GEOM_POSSIBLE_NAMES=location")

Reading the status logs

logs<-readRDS("data/logs_fread.rds")

To calculate the participants at restaurants, places of work, pubs, we are performing an inner join to get the exact location of the participant at a given instance.

PartatRestaurants<-inner_join(x=logs,
          y=restaurantcsv,
          by=c('currentLocation' = 'location'))

PartatEmployers<-inner_join(x=logs,
          y=employerscsv,
          by=c('currentLocation' = 'location'))

PartatPubs<-inner_join(x=logs,
          y=pubcsv,
          by=c('currentLocation' = 'location'))

Let us also go through the Participants file for us to revisit the age groups and educational background.

Participants<-read_csv("data/Participants.csv")
ParticipantsAgeRegrouped<-Participants%>%mutate(agegroup=case_when(age<30~"Below 30",

                                    age>=30 &age<40~"30-39",
                                    age>=40 &age<50~"40-49",
                                    age>=50 ~"50 and above"))

Checking for Sampling bias

Before we continue with the data analysis, it is necessary for us to check for biasing. Our objective here is to figure out if the data provided to us has any kind of Sampling bias. To verify this, we can look at the residential status of the participants.

Any sampling detected (for example- majority of the participants are residing in one side of the city) may affect our observations.

Data Preparation

For this, we will use the logs to find out the count of participants in the apartments and map it with the corresponding building file.

PartatApartments<-logs%>%
  distinct(apartmentId,participantId)%>%
  group_by(apartmentId)%>%
  tally()%>%
  mutate(apartmentId=as.character(apartmentId))

apartmentdetails<-left_join(apartmentscsv%>%
                        mutate(apartmentId=as.character(apartmentId)),
                        PartatApartments,
                        by=c("apartmentId"="apartmentId"))

buildingdetails<-apartmentdetails%>%
  group_by(buildingId)%>%
  summarise(residents=sum(n))
write_csv(buildingdetails,"data/buildingdetails.csv")

2. Reading the building details file

ReadingBuildingDetails<-read_csv("data/buildingdetails.csv")

3. Calculating the building occupancy

buildingoccupancy<-left_join(buildings,
                        ReadingBuildingDetails%>%
                        mutate(buildingId=as.character(buildingId)),
                        by=c("buildingId"="buildingId"))

Visualization

tmap_mode("plot")
tm_shape(buildingoccupancy)+
tm_polygons(col = "residents",
           border.col = "grey",
           style="cont",
           palette = "Blues",
           border.lwd = 1,
           border.alpha = 0.5,
           colorNA = "white")+
  tm_layout(frame=F,
            main.title = "Do we see any discrmination?",
            main.title.size = 2,
            legend.position = c("right","top"),
            legend.height = 0.2,
            legend.width = 0.2)+
  tm_compass()+
  tm_credits("Based on the sample of 1000 participants",
             position = c("left","bottom"))

Observations:

• When it comes to location, we can conclude that the data provided to us is quite diverse- we have the details of people staying in various parts of the city
• Talking about homogeneity (concentration of participants in a particular part of the city), we see that the data presented to us is quite uniform as we have a nearly equal number of residents in each part of the city.

Now that we can confirm that the data is quite uniform, we can begin with our analysis answering the following questions:-

1. Can we say the places where most people live in are more socially happening?
2. What is the traffic situation during the day if most of the people are staying close to their place of work?

Data Visualization

Can we identify ‘Silicon Valley’ of Engagement?

Data preparation

Here we are going to study the commercially happening place of the city.Assuming that the working hours are 8 am - 8 pm

1. Using the Employers file to figure out the activity during the day.

EmployeesAtWork<-PartatEmployers%>%
  mutate(StartTime=format(timestamp,"%H:%M:%S"),
                        weekday=weekdays(timestamp))%>%
  filter(weekday!="Saturday" & weekday!="Sunday")%>%
         mutate(AtWork=case_when(
           StartTime>="08:00:00" & StartTime<="20:00:00"~"Yes",
           TRUE~"No"
         ))

2. Now that we have the Time Of Day, we will calculate the number of people at the particular point in the given instance using the group_by clause

QuantityOfPoints<-EmployeesAtWork%>%filter(AtWork=="Yes")%>%
  group_by(currentLocation,participantId)%>%
  tally()%>%
  mutate(participantId=as.character(participantId))
EmployeesEducationLevel<-left_join(QuantityOfPoints,
                                   ParticipantsAgeRegrouped%>%
                                     mutate(
                                       participantId=as.character(participantId)),
        by=c("participantId"="participantId"))
QuantityOfEmployeesEduLevel<-EmployeesEducationLevel%>%
  group_by(currentLocation,educationLevel)%>%
  tally()

3. Writing the data in csv format to convert it to sf format

write_csv(QuantityOfEmployeesEduLevel,"data/PartAtEmployers.csv")

4. Reading the file in sf format

z_sf<-read_sf("data/PartAtEmployers.csv",options = "GEOM_POSSIBLE_NAMES=currentLocation")

Visualization

tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "white",
           border.col = "grey",
           border.lwd = 1) +
  
tm_shape(z_sf%>%mutate(n=as.numeric(n)))+
  
tm_dots(col="educationLevel",
        size="n",
        palette="Set1")+
  tm_layout(main.title = "Where is the work?*",
            
    main.title.size = 2,
            legend.height = 0.3,
            legend.width = 0.3,
            legend.outside = FALSE,
            legend.position = c("right", "top"),
            frame = FALSE)+
  tm_compass()+
  tm_credits("*Observed from Mon-Fri at 8 am-8 pm",
             position=c("left", "bottom"))

Observation:

• The central part of the city seems to be more commercially active as it has more number of employment opportunities
• The North Side on the other hand has more people working in. We see an establishment in particular where we most number of participants of various educational qualifications working.
• We see many people who have High School and/or college level education qualifications in the city and they seem to be concentrated in the central region. This tells us that these establishments may hire part time students who are pursing their higher education and want to earn some extra bucks.

Weekday vs Weekend rush at the restaurants

People like to dine out and enjoy meals. We can study and figure out what is footfall during the weekday and weekend.

Data Preparation

1. We can start off with creating a new column ‘weekday’ which tells us day of the week from the time stamp given and write the data in csv format.

PartatRestaurants_updated<-PartatRestaurants%>%
  mutate(StartTime=format(timestamp,"%H:%M:%S"),
                        weekday=weekdays(timestamp))%>%
  group_by(weekday,restaurantId,currentLocation,foodCost)%>%
  tally()%>%
  mutate(Day=case_when(weekday != "Saturday" & weekday != "Sunday"~"Weekday",
                          weekday=="Saturday" | weekday=="Sunday"~"Weekend"))


write_csv(PartatRestaurants_updated,"data/w.csv")

2. Reading the file in sf format

PartatRestaurants_updatedSF<-read_sf("data/w.csv",options = "GEOM_POSSIBLE_NAMES=currentLocation")

Visualization

tmap_mode("view")
tm_shape(buildings)+
tm_polygons(col = "white",
           border.col = "grey",
           border.lwd = 1) +
tm_shape(PartatRestaurants_updatedSF%>%
           select(-weekday)%>%
           mutate(n=as.numeric(n),
                  restaurantId=as.character(restaurantId),
                  foodCost=as.numeric(foodCost)))+
tm_bubbles("n",col="foodCost",popup.vars=c("restaurantId"))+
  tm_facets(c("Day"),nrow=1,ncol=2, sync = TRUE)

Observations:

• People like to dine in at expensive places during the weekend as we can see a surge in the footfall in the weekend.
• The inexpensive restaurants must be near the employment sites as they seem to be more active during the weekdays than weekends

Is it all work and no play?

Data Preparation

1. Selecting those participants who are at recreation

recreationalplaces<-logs%>%
 filter(currentMode=="AtRecreation")%>%
  distinct(currentLocation,participantId)
write_csv(recreationalplaces,"data/PartRecreational.csv")

2. Checking to confirm if participants like to stay or travel between recreational spots by converting point to linestring using st_cast()

R<-read_sf("data/PartRecreational.csv",options = "GEOM_POSSIBLE_NAMES=currentLocation")
A<-R%>%distinct(currentLocation)
l<-R%>%
  group_by(participantId)%>%tally()%>%
  filter(n>=2)%>%st_cast("LINESTRING")
  
PartAgeGroup<-left_join(l,ParticipantsAgeRegrouped%>%mutate(participantId=as.character(participantId)),
        by=c("participantId"="participantId"))

Visualzation

tmap_mode("view")
tm_shape(buildings)+
tm_polygons(col = "buildingType",
           border.col = "grey",
           border.lwd = 1)+
tm_shape(PartAgeGroup)+
  tm_lines(col="agegroup",
           palette="Set2")+

tm_shape(R)+
  tm_dots()

Observations:

First Impressions
• We can identify major recreational places in the city as we find a high concentration of people on these places
• Youngsters (Below 30) are more fond of switching recreational places with time and exploring the city

On zooming in
• When we say recreational places, they may not always mean places such as malls, pubs, cafe, etc
• The lines between points tell us something interesting- People across all age groups like to change their recreational spots. They do not like to stick to their nearest recreational spots and often opt for a change in place.
• To avoid this kind of concentration, the authorities can work on building more recreational spots which may decrease the traffic as well.

Now that we have introduced traffic, let us understand the traffic conditions in the city and how it can be addressed.

Understanding the traffic conditions

Data Preparation

1. Based on the data set provided, we will filter those records where the current Mode is Transport, calculate the density using group_by and then write it in csv format.

Traffic<-logs%>%filter(currentMode=="Transport")%>%
  mutate(StartTime=format(timestamp,"%H:%M:%S"),
                        weekday=weekdays(timestamp),
         TimeOfDay=case_when(
           StartTime>="08:00:00" & StartTime<="09:00:00"~"Morning",
           StartTime>="18:00:00" & StartTime<="19:00:00"~"Evening",
         ))%>%group_by(TimeOfDay,currentLocation)%>%tally()
write_csv(Traffic,"data/trafficdata.csv")

ReadingTrafficData<-read_sf("data/trafficdata.csv",options = "GEOM_POSSIBLE_NAMES=currentLocation")
MorningTrafficData_updated<-ReadingTrafficData%>%mutate(n=as.numeric(n))%>%
  filter(TimeOfDay=="Morning")
EveningTrafficData_updated<-ReadingTrafficData%>%mutate(n=as.numeric(n))%>%
  filter(TimeOfDay=="Evening")

Visualization

Since we are dealing with density, we will create a box map

Creating a function which helps us determine the break points.

boxbreaks <- function(v,mult=1.5) {
  qv <- unname(quantile(v[[1]]))
  iqr <- qv[4] - qv[2]
  upfence <- qv[4] + mult * iqr
  lofence <- qv[2] - mult * iqr
  # initialize break points vector
  bb <- vector(mode="numeric",length=7)
  # logic for lower and upper fences
  if (lofence < qv[1]) {  # no lower outliers
    bb[1] <- lofence
    bb[2] <- floor(qv[1])
  } else {
    bb[2] <- lofence
    bb[1] <- qv[1]
  }
  if (upfence > qv[5]) { # no upper outliers
    bb[7] <- upfence
    bb[6] <- ceiling(qv[5])
  } else {
    bb[6] <- upfence
    bb[7] <- qv[5]
  }
  bb[3:5] <- qv[2:4]
  return(bb)
}

get.var <- function(vname,df) {
  v <- df[vname] %>% st_set_geometry(NULL)
  v <- unname(v[,1])
  return(v)
}


breakss <- function(vnam, df, 
                   legtitle=NA,
                   mtitle="Box Map",
                   mult=1.5){
  var <- get.var(vnam,df)
  bbb <- boxbreaks(var)
return(bbb)  
}

Plotting the map

tmap_mode("plot")
MorningTraffic<-tm_shape(buildings)+
tm_polygons(col = "buildingType",
           border.col = "grey",
           border.lwd = 1) +
tm_shape(MorningTrafficData_updated)+
  
tm_dots(size=0.05,col="n",
           border.lwd=NA,
        breaks=breakss("n",MorningTrafficData_updated),
        palette="Reds",
        labels = c("lower outlier", 
                     "< 25%", 
                     "25% - 50%", 
                     "50% - 75%",
                     "> 75%", 
                     "upper outlier"))+
  tm_layout(main.title = "Morning Rush",
            main.title.size = 2,
            legend.height = 0.2,
            legend.width = 0.2,
            legend.outside = FALSE,
            legend.position = c("right", "top"),
            frame = FALSE)+
  tm_credits("Morning peak: 8 am-9 am",
             position=c("left","bottom"),
             size=0.5)


EveningTraffic<-tm_shape(buildings)+
tm_polygons(col = "buildingType",
           border.col = "grey",
           border.lwd = 1) +
tm_shape(EveningTrafficData_updated)+
  
tm_dots(size=0.05,col="n",
           border.lwd=NA,
        breaks=breakss("n",EveningTrafficData_updated),
        palette="Reds",
        labels = c("lower outlier", 
                     "< 25%", 
                     "25% - 50%", 
                     "50% - 75%",
                     "> 75%", 
                     "upper outlier"))+
  tm_layout(main.title = "Evening Rush",
            main.title.size =2,
            legend.height = 0.2,
            legend.width = 0.2,
            legend.outside = FALSE,
            legend.position = c("right", "top"),
            frame = FALSE)+
  tm_credits("Evening peak: 6 pm-7 pm",
             position=c("left","bottom"),
             size=0.5)+
  tm_compass()

tmap_arrange(MorningTraffic,EveningTraffic,outer.margins = 0.02)

Observations

• All the traffic seems to be concentrated on the commercial places in the morning and towards the residential places in the evenings.
• Most of the traffic seems to be concentrated towards the north west and south in the mornings and more on the central region in the evenings
• If the authorities decide to work on introducing local trains to address the bottlenecks, they can start off with their trial runs in such regions which are more commercially active